Reduction and Slicing of Hierarchical State Machines Mats Heimdahl et al. University of Minnesota
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Transcript of Reduction and Slicing of Hierarchical State Machines Mats Heimdahl et al. University of Minnesota
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Reduction and Slicing of Hierarchical State MachinesMats Heimdahl et al.
University of Minnesota
Presented by Tom McMullenFor CISC836
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Outline
• Primer • Problem Space• Paper Overview• Application (Case Study)• Limitations• Critique• Discussion
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Primer
• Presented at 1997 Proceedings 6th European Software Engineering Conference
• Cited by 65 (source: Google Scholar)
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Problem Space
• Formal Specification Languages– Difficult to understand and use– Not well understood by application experts
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Problem Space
• State Machine Representation– Better, but…
Inevitable Complexity for large systems
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Problem Space
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What this paper proposes…
• Address complexity of HSMs• In Order To:– Present information in digestible chunks
• Method:– Step 1: Simplify based on scenario– Step 2: Slice for desired values
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A Quick Refresher…
• Hierarchical State Machines (HSM)
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A Quick Refresher…
• Slicing
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A quick refresher…
• Program Slicing
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Application
• Applied to HSMs– RSML (Requirements State Machine Language)• Specification of safety-critical systems
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RSML
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Testbed Specification
• TCAS II– Traffic alert and Collision Avoidance System
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Step 1: Reduce
• Produce simplified RSML model• Interpretation based on scenario– Domain restriction of next-state relation– How do we classify an intruder who has stopped
reporting altitude?
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Step 1: Reduce
• Eliminate infeasible columns for scenario• Reduction Algorithm limited to enumerated vars
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Step 2: Slice
• Slicing Algorithm– Based on marking of Abstract Syntax Tree– RSML parser part of earlier research
• Data Flow Slices (if a transition can be taken)– Data Dependency of Guarding Transition
• Control Flow Slices (when a transition is taken)– Generation of a trigger event
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Step 3: Profit?...
• Iterative slices are then combined• Attempt to answer our questions• Reduced complexity• Increased Understandability
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Case Study
• Applied to most complex part of TCAS II RSML Model
• Subjective notion of complexity / understandability
• Metrics:– # of transitions– Perceived Table Size– Effective Table Size
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Case Study
• Results– Promising• Significant reduction in table size
– But..• Reduction in # of transitions not as expected
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Critique
• Case Study details lacking– Which TCAS model? What Questions/Scenario?– 1998 paper has a more detailed case
study/empirical data• Too much emphasis on RSML– Small application– More detail on slicing HSM in general
• Reduction and Slicing algorithms not presented– Data flow / control flow slices not explained in depth
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Discussion